P1205: A LIVING PATIENT-DERIVED BIOREPOSITORY FOSTERING MICROENVIRONMENT DISSECTION IN T-CELL LYMPHOMA
Authors: Danilo Fiore, Luca Vincenzo Cappelli, Liu Zhaoqi, Nikita Kotlov, Maria Sorokina, Jude Phillip, Paul Zumbo, Liron Yoffe, Han Xueshuai, Claudia Pignataro, Paola Ghione, Valentina Fragliasso, Gerolama Condorelli, Robin Foà, Joshua Brody, Doron Betel, Wing C Chan, Wayne Tam, Anastasie Nikitina, Aleksander Bagaev, Nathan Fowler, David Weinstock, Leandro Cerchietti, Raul Rabadan, Steve Horwitz, Giorgio Inghirami
Journal: HemaSphere (2023)

Topic: 20. Lymphoma Biology & Translational Research Background: The contribution of the tumor microenvironment (TME) to Peripheral T-Cell Lymphomas (PTCL) pathogenesis has been increasingly recognized. Despite recent advancements, current therapies are not adequately tailored to individual PTCL patients, and selecting treatments according to molecularly defined individuals is an unmet medical need. Patient-Derived-Xenografts (PDX) represent promising tools to model human cancer. Notably, PDX grow within a rich microenvironment, providing new opportunities to dissect the protumorigenic role of cancer niches. Aims: 1. Assess the potential of microenvironment-related signatures to predict PTCL prognosis; 2. Molecularly and functionally dissect the bidirectional crosstalk between lymphoma and stromal cells in PTCL; 3. Implement novel pre-clinical strategies for PTCL eradication. Methods: 362 primary PTCL samples were implanted to generate PDX representative of different PTCL subtypes. For the in vitro studies, PDX were digested to isolate tumor and stromal cells. Lymphoma cells viability, in co-culture or not with stromal cells, was detected with propidium iodide staining. Total RNA was extracted with TRIZOL and sequenced using the TruSeq-Stranded Total RNA sample preparation kit. We previously developed a bioinformatic pipeline for TME classification using RNA-sequencing data (Kotlov et al, 2021). For in-vivo experiments, tumors were implanted in NSG mice, and compounds were administered after engraftment. Tumor burden was evaluated by digital caliper or MRI. Results: First, we implemented a bioinformatic-based TME classification of 845 PTCL (including our and publicly available PTCL RNA sequences) and identified 4 major TME clusters. PTCL diagnoses were distributed across the 4 TME categories without any specific association. Interestingly, the “Th2-like” TME category was associated with a poor OS. We then applied this approach to PDX, finding the same 4 distinct TME categories. To functionally validate the TME role in TCL pathogenesis, stromal cells were isolated from PDX and incubated with freshly isolated PDX lymphoma cells. We demonstrated that the co-culture with matched stromal cells improved lymphoma survival. Transcriptionally, co-cultured stromal cells displayed the upregulation of signatures controlling biogenesis, migration, cell mobility, and DNA replication, closely mimicking the freshly isolated stromal cells. These signatures were partially lost when PDX-derived stromal cells were cultured alone. Then, we tested 40 drugs using a high-throughput flow cytometry-based co-culture platform to test whether stromal cells could modulate the therapeutic efficacy. The screens showed that stromal cells could significantly improve the lymphoma viability against selected compounds. Finally, we implemented in-vivo precision medicine-driven pre-clinical trials in PDX by integrating phenotypic, genomic and drug screening data. We envisioned combination protocols aiming either to debulk tumors and in parallel to eliminate the minimal residual disease protected in the cancer niche. Summary/Conclusion: These studies confirmed the relationship between lymphoma and microenvironmental cells. They pointed out distinct TME functional subgroups and the relevant role of stromal cells in the PTCL maintenance and therapeutic response. Our in vitro system provides a model for elucidating stromal:tumor cell interactions, allowing us to explore the pathogenetic mechanisms leading to drug resistance. We predict our data will propel patient-tailored curative strategies targeting tumor cells and the supporting niche.Keywords: T cell lymphoma, Microenvironment, Drug resistance, Mouse model